OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Graph Convolution Networks for Traffic Flow Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

Traffic flow forecasting is a highly challenging task due to the dynamic spatial-temporal road conditions. Graph neural networks (GNN) has been widely applied in this task. However, most of these GNNs ignore the effects of time-varying road conditions due to the fixed range of the convolution receptive field. In this paper, we propose a novel Adaptive Graph Convolution Networks (AGC-net) to address this issue in GNN. The AGC-net is constructed by the Adaptive Graph Convolution (AGC) based on a novel context attention mechanism, which consists of a set of graph wavelets with various learnable scales. The AGC transforms the spatial graph representations into time-sensitive features considering the temporal context. Moreover, a shifted graph convolution kernel is designed to enhance the AGC, which attempts to correct the deviations caused by inaccurate topology. Experimental results on two public traffic datasets demonstrate the effectiveness of the AGC-net\footnote{Code is available at: https://github.com/zhengdaoli/AGC-net} which outperforms other baseline models significantly.
Procedurally generating rules to adapt difficulty for narrative puzzle games
Venue: arXiv cs.HC 2023
Year: 2023

This paper focuses on procedurally generating rules and communicating them to players to adjust the difficulty. This is part of a larger project to collect and adapt games in educational games for young children using a digital puzzle game designed for kindergarten. A genetic algorithm is used together with a difficulty measure to find a target number of solution sets and a large language model is used to communicate the rules in a narrative context. During testing the approach was able to find rules that approximate any given target difficulty within two dozen generations on average. The approach was combined with a large language model to create a narrative puzzle game where players have to host a dinner for animals that can't get along. Future experiments will try to improve evaluation, specialize the language model on children's literature, and collect multi-modal data from players to guide adaptation.
The Ethical Implications of Generative Audio Models: A Systematic Literature Review
Venue: arXiv cs.CY 2023
Year: 2023

Generative audio models typically focus their applications in music and speech generation, with recent models having human-like quality in their audio output. This paper conducts a systematic literature review of 884 papers in the area of generative audio models in order to both quantify the degree to which researchers in the field are considering potential negative impacts and identify the types of ethical implications researchers in this area need to consider. Though 65% of generative audio research papers note positive potential impacts of their work, less than 10% discuss any negative impacts. This jarringly small percentage of papers considering negative impact is particularly worrying because the issues brought to light by the few papers doing so are raising serious ethical implications and concerns relevant to the broader field such as the potential for fraud, deep-fakes, and copyright infringement. By quantifying this lack of ethical consideration in generative audio research and identifying key areas of potential harm, this paper lays the groundwork for future work in the field at a critical point in time in order to guide more conscientious research as this field progresses.
Keystroke Dynamics for User Identification
Venue: arXiv cs.LG 2023
Year: 2023

In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. In this research, we consider the more challenging multiclass user identification problem, based on free-text data. We experiment with a complex image-like feature that has previously been used to achieve state-of-the-art authentication results over free-text data. Using this image-like feature and multiclass Convolutional Neural Networks, we are able to obtain a classification (i.e., identification) accuracy of 0.78 over a set of 148 users. However, we find that a Random Forest classifier trained on a slightly modified version of this same feature yields an accuracy of 0.93.
Face Image Quality Enhancement Study for Face Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Unconstrained face recognition is an active research area among computer vision and biometric researchers for many years now. Still the problem of face recognition in low quality photos has not been well-studied so far. In this paper, we explore the face recognition performance on low quality photos, and we try to improve the accuracy in dealing with low quality face images. We assemble a large database with low quality photos, and examine the performance of face recognition algorithms for three different quality sets. Using state-of-the-art facial image enhancement approaches, we explore the face recognition performance for the enhanced face images. To perform this without experimental bias, we have developed a new protocol for recognition with low quality face photos and validate the performance experimentally. Our designed protocol for face recognition with low quality face images can be useful to other researchers. Moreover, experiment results show some of the challenging aspects of this problem.
Concerto Grosso for Sessions: Fair Termination of Sessions
Venue: arXiv cs.LO 2023
Year: 2023

Sessions are a fundamental notion in message-passing systems. A session is an abstract notion of communication between parties where each one owns an endpoint. Session types are types that are assigned to the endpoints and that are used to statically and dynamically enforce some desired properties of the communications, such as the absence of deadlocks. Properties of concurrent systems are usually divided in safety and liveness ones and depending on the class it belongs to, a property is defined using different (dual) techniques. However, there exist some properties that require to mix both techniques and the challenges in defining them are exacerbated in proof assistants (e.g. Agda, Coq, . . . ), that is, tools that allow users to formally characterize and prove theorems. In particular, we mechanize the meta-theory of inference systems in Agda. Among the interesting properties that can be studied in the session-based context, we study fair termination which is the property of those sessions that can always eventually reach successful termination under a fairness assumption. Fair termination implies many desirable and well known properties, such as lock freedom. Moreover, a lock free session does not imply that other sessions are lock free as well. On the other hand, if we consider a session and we assume that all the other ones are fairly terminating, we can conclude that the one under analysis is fairly terminating as well.
Type-Preserving Compilation of Class-Based Languages
Venue: arXiv cs.PL 2023
Year: 2023

The Dependent Object Type (DOT) calculus was designed to put Scala on a sound basis, but while DOT relies on structural subtyping, Scala is a fundamentally class-based language. This impedance mismatch means that a proof of DOT soundness by itself is not enough to declare a particular subset of the language as sound. While a few examples of Scala snippets have been manually translated into DOT, no systematic compilation scheme has been presented so far. In this thesis we develop a series of calculi of increasing complexity to model Scala and present a type-preserving compilation scheme from each of these calculus into DOT. Along the way, we develop some necessary extensions to DOT.
TransPose: A Transformer-based 6D Object Pose Estimation Network with Depth Refinement
Venue: arXiv cs.CV 2023
Year: 2023

As demand for robotics manipulation application increases, accurate vision-based 6D pose estimation becomes essential for autonomous operations. Convolutional Neural Networks (CNNs) based approaches for pose estimation have been previously introduced. However, the quest for better performance still persists especially for accurate robotics manipulation. This quest extends to the Agri-robotics domain. In this paper, we propose TransPose, an improved Transformer-based 6D pose estimation with a depth refinement module. The architecture takes in only an RGB image as input with no additional supplementing modalities such as depth or thermal images. The architecture encompasses an innovative lighter depth estimation network that estimates depth from an RGB image using feature pyramid with an up-sampling method. A transformer-based detection network with additional prediction heads is proposed to directly regress the object's centre and predict the 6D pose of the target. A novel depth refinement module is then used alongside the predicted centers, 6D poses and depth patches to refine the accuracy of the estimated 6D pose. We extensively compared our results with other state-of-the-art methods and analysed our results for fruit-picking applications. The results we achieved show that our proposed technique outperforms the other methods available in the literature.
Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion
Venue: arXiv cs.CL 2023
Year: 2023

This paper describes our zero-shot approaches for the Visual Word Sense Disambiguation (VWSD) Task in English. Our preliminary study shows that the simple approach of matching candidate images with the phrase using CLIP suffers from the many-to-many nature of image-text pairs. We find that the CLIP text encoder may have limited abilities in capturing the compositionality in natural language. Conversely, the descriptive focus of the phrase varies from instance to instance. We address these issues in our two systems, Augment-CLIP and Stable Diffusion Sampling (SD Sampling). Augment-CLIP augments the text prompt by generating sentences that contain the context phrase with the help of large language models (LLMs). We further explore CLIP models in other languages, as the an ambiguous word may be translated into an unambiguous one in the other language. SD Sampling uses text-to-image Stable Diffusion to generate multiple images from the given phrase, increasing the likelihood that a subset of images match the one that paired with the text.
Learning From Each Other: How Are Architectural Mistakes Communicated in Industry?
Venue: arXiv cs.SE 2023
Year: 2023

Context. Own experiences and faulty decisions can be an important source of information for software architects. The experiences and mistakes of other architects can also be valuable information sources. Goal. Under the assumption that the knowledge about faulty decisions, i.e., mistakes, regarding software architecture is not shared adequately in practice, this work qualitatively investigates the handling and particularly communication of those mistakes by software architects. Method. We conducted a grounded-theory study in which we interviewed ten German software architects from various domains. Results. We identified software architects' definitions of architectural mistakes, their handling of these mistakes, and their preferred communication strategies regarding these mistakes. We found that architects communicate mistakes mainly within their project teams and seldom within or across companies. Conclusions. We derived strategies to make learning and prevention of mistakes more effective. To share experiences and knowledge beyond architects' peer groups, companies should invest more effort in discussing mistakes more consciously and create an environment where mistakes can be discussed openly.
DBFed: Debiasing Federated Learning Framework based on Domain-Independent
Venue: arXiv cs.LG 2023
Year: 2023

As digital transformation continues, enterprises are generating, managing, and storing vast amounts of data, while artificial intelligence technology is rapidly advancing. However, it brings challenges in information security and data security. Data security refers to the protection of digital information from unauthorized access, damage, theft, etc. throughout its entire life cycle. With the promulgation and implementation of data security laws and the emphasis on data security and data privacy by organizations and users, Privacy-preserving technology represented by federated learning has a wide range of application scenarios. Federated learning is a distributed machine learning computing framework that allows multiple subjects to train joint models without sharing data to protect data privacy and solve the problem of data islands. However, the data among multiple subjects are independent of each other, and the data differences in quality may cause fairness issues in federated learning modeling, such as data bias among multiple subjects, resulting in biased and discriminatory models. Therefore, we propose DBFed, a debiasing federated learning framework based on domain-independent, which mitigates model bias by explicitly encoding sensitive attributes during client-side training. This paper conducts experiments on three real datasets and uses five evaluation metrics of accuracy and fairness to quantify the effect of the model. Most metrics of DBFed exceed those of the other three comparative methods, fully demonstrating the debiasing effect of DBFed.
Active Learning for Video Classification with Frame Level Queries
Venue: arXiv cs.CV 2023
Year: 2023

Deep learning algorithms have pushed the boundaries of computer vision research and have depicted commendable performance in a variety of applications. However, training a robust deep neural network necessitates a large amount of labeled training data, acquiring which involves significant time and human effort. This problem is even more serious for an application like video classification, where a human annotator has to watch an entire video end-to-end to furnish a label. Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data; this tremendously reduces the human annotation effort in inducing a machine learning model, as only the few samples that are identified by the algorithm, need to be labeled manually. In this paper, we propose a novel active learning framework for video classification, with the goal of further reducing the labeling onus on the human annotators. Our framework identifies a batch of exemplar videos, together with a set of informative frames for each video; the human annotator needs to merely review the frames and provide a label for each video. This involves much less manual work than watching the complete video to come up with a label. We formulate a criterion based on uncertainty and diversity to identify the informative videos and exploit representative sampling techniques to extract a set of exemplar frames from each video. To the best of our knowledge, this is the first research effort to develop an active learning framework for video classification, where the annotators need to inspect only a few frames to produce a label, rather than watching the end-to-end video.
Collaborative Song Dataset (CoSoD): An annotated dataset of multi-artist collaborations in popular music
Venue: arXiv cs.SD 2023
Year: 2023

The Collaborative Song Dataset (CoSoD) is a corpus of 331 multi-artist collaborations from the 2010-2019 Billboard "Hot 100" year-end charts. The corpus is annotated with formal sections, aspects of vocal production (including reverberation, layering, panning, and gender of the performers), and relevant metadata. CoSoD complements other popular music datasets by focusing exclusively on musical collaborations between independent acts. In addition to facilitating the study of song form and vocal production, CoSoD allows for the in-depth study of gender as it relates to various timbral, pitch, and formal parameters in musical collaborations. In this paper, we detail the contents of the dataset and outline the annotation process. We also present an experiment using CoSoD that examines how the use of reverberation, layering, and panning are related to the gender of the artist. In this experiment, we find that men's voices are on average treated with less reverberation and occupy a more narrow position in the stereo mix than women's voices.
Virtual Network Embedding without Explicit Virtual Network Specification
Venue: arXiv cs.NI 2023
Year: 2023

Network virtualization enables Internet service providers to run multiple heterogeneous and dedicated network architectures for different customers on a shared substrate. In existing works on virtual network embedding (VNE), each customer formulates a virtual network request (VNR) where a virtual network (VN) is required. Motivated by a concrete example where VN is not a proper VNR formulation to reflect the traffic demand of a customer, we propose a new VNR formulation described by the traffic demand between several access node pairs to complement the existing VNR formulation. Moreover, three different groups of VNE variants are systematically examined. Simulations demonstrate that shared channel embedding, as a new embedding variant under the proposed VNR formulation, improves the acceptance rate and reduces cost and link utility compared to traditional independent channel embedding.
Transaction Fraud Detection via an Adaptive Graph Neural Network
Venue: arXiv cs.LG 2023
Year: 2023

Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods leverage original features only or require manual feature engineering. They lack the ability to learn discriminative representations from transaction data. Moreover, criminals often commit fraud by imitating cardholders' behaviors, which causes the poor performance of existing detection models. In this paper, we propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Specifically, we leverage cosine similarity and edge weights to adaptively select neighbors with similar behavior patterns for target nodes and then find multi-hop neighbors for fraudulent nodes. A neighbor diversity metric is designed by calculating the entropy among neighbors to tackle the camouflage issue of fraudsters and explicitly alleviate the over-smoothing phenomena. Extensive experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Customer feedback is invaluable to companies as they refine their products. Monitoring customer feedback can be automated with Aspect Level Sentiment Classification (ALSC) which allows us to analyse specific aspects of the products in reviews. Large Language Models (LLMs) are the heart of many state-of-the-art ALSC solutions, but they perform poorly in some scenarios requiring Coreference Resolution (CR). In this work, we propose a framework to improve an LLM's performance on CR-containing reviews by fine tuning on highly inferential tasks. We show that the performance improvement is likely attributed to the improved model CR ability. We also release a new dataset that focuses on CR in ALSC.
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models
Venue: arXiv cs.IR 2023
Year: 2023

Recommender system is adored in the internet industry as one of the most profitable technologies. Unlike other sectors such as fraud detection in the Fintech industry, recommender system is both deep and broad. In recent years, many researchers start to focus on the cold-start problem of recommender systems. In spite of the large volume of research literature, the majority of the research utilizes transfer learning / meta learning and pretrained model to solve the problem. Although the researchers claim the effectiveness of the approaches, everyone of them does rely on extra input data from other sources. In 2021 and 2022, several zeroshot learning algorithm for recommender system such as ZeroMat, DotMat, PoissonMat and PowerMat were invented. They are the first batch of the algorithms that rely on no transfer learning or pretrained models to tackle the problem. In this paper, we follow this line and invent a new zeroshot learning algorithm named LogitMat. We take advantage of the Zipf Law property of the user item rating values and logistic regression model to tackle the cold-start problem and generate competitive results with other competing techniques. We prove in experiments that our algorithm is fast, robust and effective.
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
Venue: arXiv cs.AI 2023
Year: 2023

Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for behavior graph understanding in job recommendations remains largely unexplored. This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application. We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs and uncover underlying patterns and relationships. Specifically, we propose a meta-path prompt constructor that leverages LLM recommender to understand behavior graphs for the first time and design a corresponding path augmentation module to alleviate the prompt bias introduced by path-based sequence input. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users. We evaluate the effectiveness of our approach on a comprehensive dataset and demonstrate its ability to improve the relevance and quality of recommended quality. This research not only sheds light on the untapped potential of large language models but also provides valuable insights for developing advanced recommendation systems in the recruitment market. The findings contribute to the growing field of natural language processing and offer practical implications for enhancing job search experiences. We release the code at https://github.com/WLiK/GLRec.
Approximating the Value of Energy-Parity Objectives in Simple Stochastic Games
Venue: arXiv cs.GT 2023
Year: 2023

We consider simple stochastic games $\mathcal G$ with energy-parity objectives, a combination of quantitative rewards with a qualitative parity condition. The Maximizer tries to avoid running out of energy while simultaneously satisfying a parity condition. We present an algorithm to approximate the value of a given configuration in 2-NEXPTIME. Moreover, $\varepsilon$-optimal strategies for either player require at most $O(2EXP(|{\mathcal G}|)\cdot\log(\frac{1}{\varepsilon}))$ memory modes.